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CAREER: Scalable Algorithms for Extreme Computing on Heterogeneous Hardware, with Applications in Fluids and Biology

CAREER: Scalable Algorithms for Extreme Computing on Heterogeneous Hardware, with Applications in Fluids and Biology
职业:异构硬件上极限计算的可扩展算法,在流体和生物学中的应用
批准号:
1460035
负责人:
Lorena Barba
金额:
$40.54万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-08-01 至 2018-02-28

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中文摘要
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英文摘要
There is currently a world-wide quest to achieve exascale computing by the end of the decade, with vigorous efforts in the US as well as China and Japan, in particular. In the US, the President's Strategy for American Innovation (2009) explicitly lists among its goals to dramatically increase our simulations capacity via an exascale computer. The challenges to achieve this goal are unprecedented: power constraints, microchip fabrication reaching physical limits, the growing imbalance between compute capacity and interconnect bandwidth, and the ever increasing number of cores in a system.Among the matters of highest priority are development of scalable algorithms that can exploit the enormous parallelism of new systems, and educating the next generation of computational scientists. The first of these is at the center of the scientific part of this CAREER project. A potentially transformative combination is emerging where a class of hierarchical algorithms, offering ideal scaling linear with problem size, maps with excellent performance to many-core hardware (such as GPUs). Algorithmic improvements will be undertaken, such as combining elements of treecodes and fast multipole methods, communication and synchronization avoidance, dynamic error control and auto-tuning of the computation. The research program is vertically integrated across disciplines, including applications at extreme scales in fluid dynamics and biological systems.This project will produce highly scalable scientific software, reformulating the algorithms to achieve maximum performance in many-core hardware. Disseminated and curated via the open-source model, the computational infrastructure delivered will offer maximum impact, beyond the application areas of focus. Community software that is able to scale to millions of processors will be crucial to exploit post-petascale systems, and this project aims to provide that. The educational part of this program, on the other hand, builds on the PI's track record of success both in the use of technology to support learning, and in catalyzing international collaboration and outreach. The program includes enhancing educational environments using technology for both curricular instruction and contributing to the nation's science literacy (via open courseware). The goals of fostering the next generation of computational scientists will be pursued via extra-mural advanced training events, and online learning media.
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NSF-FDA: Generating trustworthy computational evidence to support FDA’s regulatory evaluation of medical devices, via transparency and reproducibility
  • 批准号:
    2040175
  • 项目类别:
    Standard Grant
  • 资助金额:
    $5.66万
  • 财政年份:
    2021
  • 负责人:
    Lorena Barba
  • 依托单位:
CyberTraining: DSE. The Code Maker: Computational Thinking for Engineers with Interactive, Contextual Learning
  • 批准号:
    1730170
  • 项目类别:
    Standard Grant
  • 资助金额:
    $50.0万
  • 财政年份:
    2017
  • 负责人:
    Lorena Barba
  • 依托单位:
EAGER: Cyberinfrastructure Reproducibility Project: Computational Science and Engineering
  • 批准号:
    1747669
  • 项目类别:
    Standard Grant
  • 资助金额:
    $29.98万
  • 财政年份:
    2017
  • 负责人:
    Lorena Barba
  • 依托单位:
CAREER: Scalable Algorithms for Extreme Computing on Heterogeneous Hardware, with Applications in Fluids and Biology
  • 批准号:
    1149784
  • 项目类别:
    Standard Grant
  • 资助金额:
    $55.06万
  • 财政年份:
    2012
  • 负责人:
    Lorena Barba
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis